Aaum's - Presentation on Social Media Analytics

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Ordo Ab Chao
leveraging social media
sentiments

2. - 2 -
Corporate profile
Founded by IIT Madras alumnus having extensive global business experience with Fortune 100
companies in United States and India having three lines of business
Prof Prakash Sai
Dr. Prakash Sai is professor at the Department
of Management Studies, Indian Institute of
Technology Madras. He has wealth of
international consulting experience in Strategy
Formulation
Puneet Gupta
Puneet spearheads the IFMR Mezzanine
Finance (Mezz Co.), is strengthening the
delivery of financial services to rural households
and urban poor by making investments in local
financial institutions.
Padma Shri Dr. Ashok Jhunjhunwala
Dr. Ashok Jhunjhunwala is Professor at the
Department of Electrical Engineering, Indian
Institute of Technology Madras India. He holds a
B.Tech degree from IIT, Kanpur, and M.S. and
Ph.D degrees from the University of Maine, USA.
Analytics
• Appropriate statistical models
through which clients can measure
and grow their business.
Competitive Intelligence
• Actionable insights to clients for
their business excellence
Livelihood
•Services ranging from promotion of
livelihoods, implementation services,
livelihood & feasibility studies.
 Key Focus Areas in Advanced analytics and Predictive analytics
 Product – geniSIGHTS (Analytics/BI), Ordo-ab-Chao (Social Media)
 More than 25 consulting assignments for Businesses & Govt orgs
 Partnership – Actuate, IIT Madras, TIE and 3 strategic partnerships
 Dedicated corporate office at IIT Madras Research park since 2009
Aaum’s office, IIT Madras Research Park

3. - 3 -
Competencies in
Advanced analytics
Build appropriate statistical models through
which clients can measure and grow their
business.
Expertise in
• Digital Media
• Finance/Insurance
• Retail
• Entertainment
• Human Capital
• Government organizations
• Research & training
Competitive
assessment
Competitive intelligence
Provide actionable insights to clients for
their business excellence.
Expertise in
•Business Entry
•Business Expansion
•Market research
Livelihood
Perform livelihood services ranging from
promotion of livelihoods, implementation
services, livelihood and feasibility studies.
Expertise in
•Government organizations
•Non Government
organizations
•Corporate with livelihood
focus
•Research

4. - 4 -
Problem DescriptionCurrent Process
Market Segments
Size of Addressable Market
 Only about half of the businesses using social
media networks like Facebook, Twitter, or
LinkedIn have any idea as to a return on
investment.
 Businesses doesn’t know how to leverage
the insights effectively for their operations.
 Sound understanding of business objectives will
be helpful to devise appropriate methodologies
to extract/analyze information from web.
 Many players existing to categorize sentiments
based on text mining techniques but no solution
existing on predicting social media based
on advanced analytics real time to benefit
the business goals.
Articulating the current scenario in social media
TheProblem
 Most of the businesses have presence in web, get
feedback to gather from comments and derive
insights manually or by leveraging software (just
classification) available in the market.
 All business segments – No differentiation
 Assuming 40% of the companies have presence
in the web. And 50 % of them have adopted
social media or using social media techniques.
This is a very captive market looking at the
impact made by social media in the recent years.

5. - 5 -
Solution DescriptionProposed Process
Market Segments
Size of Addressable Market
 Aaum have developed a working concept to
predict/categorize social media comments by
various business parameters.
 Customized advanced analytical algorithms,
dictionary have been developed to accomplish
this.
 This product has potential to change the
business paradigm.
 With continuous data mining and right infrastructure,
it is now possible to know people’s choice specific to
the business.
 Just the tip of the iceberg and Sky is the limit!
 Please refer to the proof of concept in the next
slide.
Aaum’s solution envisioned
OurSolution
 Automatic sentiment mining from social media.
 Predict social media opinions real time based on
advanced analytics to benefit the business
objectives.
 All business segments – No differentiation
 Assuming 40% of the companies have presence
in the web. And 50 % of them have adopted
social media or using social media techniques.
This is a very captive market looking at the
impact made by social media in the recent years.
Business objectives
• Generate more word of mouth
• Increase customer loyalty
• Bring outside ideas into organization
• Increase product/brand awareness
• Improve new product success ratios
• Improve public relations effectiveness
• Reduce customer acquisition costs
• Reduce customer support costs
• Reduce market research costs
• Reduce product development costs
Prioritized business
• Generate more word of mouth <2
• Increase customer loyalty <4
• Bring outside ideas into organization <3
• Increase product/brand awareness <5
• Improve new product success ratios
• Improve public relations effectiveness <1
• Reduce customer acquisition costs
• Reduce customer support costs
• Reduce market research costs
• Reduce product development costs

6. - 6 -
Problem DescriptionCompetitive comparison
Competitive positioning by key factors
 Social media is sunrise industry.
 Many software companies are making tools to
come up with solutions to categorize the
comments based on basic text mining
techniques
 None of these tools have good predictive
solutions for the business to derive insights
for their operations.
 Aaum’s solution is a blue ocean product to
leverage the benefits of analytics in social
media.
Competitive landscape
Competition
 A few commercially available packages
 SAS, SPSS
– Performs text mining and categorizes by key words
– Adopts a generic approach
 Customized solutions (Many vendors)
 Based on dictionary classification and partially on
advanced analytics
 Visibli, Semioboard, viralheat, etc
– More of web analytics
 A comprehensive solution with continuous monitoring
is lacking in this space!
Differentiators Aaum’s solution Competition
Text mining and automatic comments extraction

Partial
Prediction by business parameters

Partial
Prediction by advanced analytical techniques and therefore better prediction/accuracy
 
Continuous mining of data and hence not limited to one time “api” search
 

7. Case Study: Comparative
Sentiment Analysis on Pongal
release movie
“What people expressed before the
release & post the release”
CaseStudy

8. - 8 -
Data collected from various sources based on the business rules
formed....

9. - 9 -
Removing the bias factor is a very important process in our approach in addition to
other data cleansing activities and transformation techniques
Username
No. of
tweets
AllAboutVijay 209
rahulsundar_007 197
AllAbtVijay 150
TheVijay360 143
VijayAnban 141
Actor_Vijay 127
techboss2011 126
j_shanujan 119
DineshSelvakuma 110
abhi_vfrndz89 106
VijayExpress 104
tamizhanlogesh 97
MelanieWF 96
Vijay_theactor 95
cinemausher 76
rohith_vijayfan 74
VIJAY_FansClub 73
may_war 71
cineandhra 61
IIAYATHALABATHY 59
Username
No. of
tweets
Dhananjayang 93
Star_falilah 84
techboss2011 51
yuvanfansclub 50
meyyappanram 45
prakash_12345 44
TamilStudios 42
crajapriyan 40
ActorMadhavan 40
amala_amsfc 39
Thus_S 30
Magenraja 29
Actor_Amalapaul 29
97thstreetswgrx 29
vigneshwar_m 27
LatchmiGirl 27
j_shanujan 27
sruthii4u 26
TweetSav 24
rajg_kumar 24
‘Nanban’ ‘Vettai’

10. - 10 -
Key insights
 Did the movie meet the expectations of the crowd
– Pre-release vs Post release
 How did the movie perform?
– 7 days before vs 7 days after
 Key Enabling factors
 Key Deteriorating factors
 Scope for improvement
– Preventive and corrective actions
 See all by yourself in our Movie-meter dashboard!!!
– Prediction based on our training algorithms

11. - 11 -
0
500
1000
1500
2000
2500
3000
Movie Hero Heroine Director S ong Movie Hero Heroine Director S ong
Nanban Vettai
Neutral Negative Pos itive
0
2000
4000
6000
8000
10000
12000
Movie Hero Heroine Director S ong Movie Hero Heroine Director S ong
Nanban Vettai
Neutral Negative Positive
0
1000
2000
3000
4000
5000
6000
Movie Hero Heroine Director S ong Movie Hero Heroine Director S ong
Nanban Vettai
Neutral Negative Positive
0
2000
4000
6000
8000
10000
12000
14000
Movie Hero Heroine Director S ong Movie Hero Heroine Director S ong
Nanban Vettai
Neutral Negative Positive
With out bias
With out biasWith bias
With bias
Post release
Pre release
Our classification techniques are based on advanced analytical algorithms

12. - 12 -
Insights from our analysis
Pre release analysis shows a very good support for both the movies.
 A very few negative criticisms
 Heavy promotions witnessed in case of Vettai
 Removal of bias factors provides an altogether different perspective!
Post release analysis also shows a very good support for both the movies.
 Nanban as clearly emerged a ‘victor’
 More positive comments about the movie, direction and the hero
 And nobody cares for songs from any of the movies!!!

13. - 13 -
Possible applications
 “Ordo Ab Chao” - our analytical product, a powerful listening tool to
mine social media sentiments
– Local movies chosen to initiate buzz and to create viral marketing
 The product demonstrated finds wide application areas especially
– FMCG companies – To understand the customer behavior and usage
patterns.
• Effectiveness of campaign launch, advertisements
– Company, Competition and Customer
• e.g. New brand launch
– Social media companies/Communities – to cater their services effectively to
their customers.
• e.g. Twitter?
– Entertainment industry - To understand the customer sentiments.
• Academy awards, News channels, etc

14. - 14 -
Want to see trends, changing audience perspectives on the movie
attributes?
vs
Take a dive into the next slides

15. - 15 -
Nanban –Insights for movie and hero
Hero
-50
0
50
100
150
200
250
300
350
12/18/2011 12/23/2011 12/28/2011 1/2/2012 1/7/2012 1/12/2012 1/17/2012 1/22/2012
Negative Neutral Positive
Hero
0 0 0 0 0 0 0 0
1
0 0
1
0 5 4 0
5 1 4 17 26 17 3 16 14 1
1 1 1
2
8 4 6
1
1
6 4
13
7
129 120
12
83
76
58
262
268
269 186
221
294
62
0 0 0
1
0 0 0
1
0 0 0 0 0
36 38
0
40
8
50
307
284
158 102
203
220
52
0%
20%
40%
60%
80%
100%
12/21/2011
12/22/2011
12/23/2011
12/24/2011
12/25/2011
12/26/2011
12/27/2011
12/28/2011
12/29/2011
12/30/2011
12/31/2011
1/1/2012
1/2/2012
1/3/2012
1/4/2012
1/5/2012
1/6/2012
1/7/2012
1/8/2012
1/9/2012
1/10/2012
1/11/2012
1/12/2012
1/13/2012
1/14/2012
1/15/2012
1/16/2012
1/17/2012
1/18/2012
Negative Neutral Positive
Movie-Nanban
0 0 0 0
1
0 0 0 0 0
1
1 13 9 0 11 9 23 96 116 132 54 74 56 13
2
1 1 1 3 3 2
1
4
11
12
68
497 612
27
837 356 458
1562
2042
1741708
11431245342
1
0 0 0
1
0 0
1
1
0 0 0
83 91
2
115 64 105
704
705
424 177
360 348 81
0%
20%
40%
60%
80%
100%
12/20/2011
12/21/2011
12/22/2011
12/23/2011
12/24/2011
12/25/2011
12/26/2011
12/27/2011
12/28/2011
12/29/2011
12/30/2011
12/31/2011
1/1/2012
1/2/2012
1/3/2012
1/4/2012
1/5/2012
1/6/2012
1/7/2012
1/8/2012
1/9/2012
1/10/2012
1/11/2012
1/12/2012
1/13/2012
1/14/2012
1/15/2012
1/16/2012
1/17/2012
1/18/2012
Negative Neutral Positive
Movie-Nanban
-500
0
500
1000
1500
2000
2500
12/18/2011 12/23/2011 12/28/2011 1/2/2012 1/7/2012 1/12/2012 1/17/2012 1/22/2012
Negative Neutral Positive
Pre & Post release insights

16. - 16 -
Director -Nanban
1
0
4 2
0
1
0 0 0
1
0
17
13
4
28
11
35
120
225
85
53
81
69
21
0
0
0 0
0
0
0 0 0
0
0
0
1
0
5
0
2
5
8
20
6
6
4
1
0
0
0 0
0
0
0 0 0
0
0
0 0 0
18
4
4
170
79
46
19
50
35
1
0%
20%
40%
60%
80%
100%
12/26/2011
12/27/2011
12/28/2011
12/29/2011
12/30/2011
12/31/2011
1/1/2012
1/2/2012
1/3/2012
1/4/2012
1/5/2012
1/6/2012
1/7/2012
1/8/2012
1/9/2012
1/10/2012
1/11/2012
1/12/2012
1/13/2012
1/14/2012
1/15/2012
1/16/2012
1/17/2012
1/18/2012
Neutral Negative Positive
Heroine -Nanban
8 1 7
8
4
8 9
16
30
14
9
42
21
6
0 0 0
0
0
0 0
10
12
0 2
1
0
00 0 0
1
3
1 1
10
8
4
2 7
1
0
0%
20%
40%
60%
80%
100%
1/4/2012
1/5/2012
1/6/2012
1/7/2012
1/8/2012
1/9/2012
1/10/2012
1/11/2012
1/12/2012
1/13/2012
1/14/2012
1/15/2012
1/16/2012
1/17/2012
1/18/2012
Neutral Negative Positive
Heroine-Nanban
0
5
10
15
20
25
30
35
40
45
50
1/3/2012 1/6/2012 1/9/2012 1/12/2012 1/15/2012 1/18/2012 1/21/2012
Negative Neutral Positive
Director-Nanban
0
50
100
150
200
250
300
12/22/2011 12/25/2011 12/28/2011 12/31/2011 1/3/2012 1/6/2012 1/9/2012 1/12/2012 1/15/2012 1/18/2012 1/21/2012
Neutral Negative Positive
Nanban –Insights for director and heroine
Pre & Post release insights

17. - 17 -
Questions/Feedback?
Contact us
01 N, 1st floor IIT Madras Research Park, Kanagam road, Chennai – 600113
Tel :` +91 44 66469877, Fax:+91 44 66469877
Email: info@aaumanalytics.com, Skype:b.rajeshkumar
Twitter: AaumAnalytics, Web: www.aaumanalytics.com
Facebook: http://www.facebook.com/AaumAnalytics
LinkedIn: http://www.linkedin.com/company/aaum-research-and-analytics-iit-madras
About Aaum
Aaum Research and Analytics founded by IIT Madras alumnus brings in extensive global business
experience working with Fortune 100 companies in North America and Asia Pacific. Incubated at IIT
Madras Incubator ecosystem with a focus on researching and devising the sophisticated analytical
techniques to solve the pressing business needs of corporations ranging from Health Care,
Entertainment, FMCGs, finance, insurance, retail, Telecom.
Aaum’s office at IIT Madras Research Park